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Background and Objective: In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice even if they could support effectively the physician during the diagnostic process. Apart from the issues related to the explainability of the predictions of a neural model, such systems are not integrated in the diagnostic workflow, and a standardization of their use is needed to achieve this goal., Methods: This paper presents IODeep a new DICOM Information Object Definition (IOD) aimed at storing both the weights and the architecture of a DNN already trained on a particular image dataset that is labeled as regards the acquisition modality, the anatomical region, and the disease under investigation., Results: The IOD architecture is presented along with a DNN selection algorithm from the PACS server based on the labels outlined above, and a simple PACS viewer purposely designed for demonstrating the effectiveness of the DICOM integration, while no modifications are required on the PACS server side. Also a service based architecture in support of the entire workflow has been implemented., Conclusion: IODeep ensures full integration of a trained AI model in a DICOM infrastructure, and it is also enables a scenario where a trained model can be either fine-tuned with hospital data or trained in a federated learning scheme shared by different hospitals. In this way AI models can be tailored to the real data produced by a Radiology ward thus improving the physician decision making process. Source code is freely available at https://github.com/CHILab1/IODeep.git., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Abstract: The Covid-19 pandemic has completely modified the Healthcare organization. This review aims to analyze the evolution of the different Telemedicine areas during pandemic. Electronic Health Records allows accelerating the study of patients suffering from Covid-19 disease, supporting their clinical assistance. The decreasing rehabilitation programs have determined a deterioration of the patient quality of life. Teleradiology was necessary to discard the increased requests and dab the shortage of staff, and to guarantee the interaction between specialist and patient. Telecardiology was fundamental determining a reduced of secondary mortality for cardiological complications of the infection. Teledermatology has permitted an early identification of the patients affected through diagnoses of cutaneous signs, reducing clinical visits. Telelegal-medicine changed through a law, that was introduced allowing a remarkable use of videoconferencing in the different stages of judicial and extrajudicial process. The digital consultations and home drug delivery were implemented in telepharmacy area. Artificial Intelligence allows an early diagnosis of the infection, monitoring the treatment through an intelligent platform. Robotic assisted telemedicine minimizes the risk of exposure allowing the disinfection of the places, drugs and meals delivery, the measurement of vital signs. Mobile Health facilitated the collection and the automatic transfer of the patient's parameters. Telemedicine would constitute still today as complementary but not substitutive to the traditional medicine. During the pandemic telemedicine has resulted important to guarantee continuity cures. Radiology and Dermatology showed a major telemedicine application.

Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator's experience. In this study, we present "blood vessel detection-BVD", an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis., (© 2022. The Author(s).)

In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different "spectra" to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands' bioactivity on a data set containing twenty protein kinases with similar binding sites to CDK1. The data set itself is presented, and the architecture is explained in detail along with its training procedure. We report experimental results and an explainability analysis to assess the contribution of each fingerprint to different targets.

Background: A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy., Results: A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination)., Conclusion: The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies.

Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investigation following a well-defined workflow. This paper proposes a software framework aimed at addressing the issues outlined before. Specifically, we designed a DICOM based mechanism of data-driven GUI generation, referring to the examined body part and imaging modality as well as to the medical image analysis task to perform. In this way, the self-configuring GUI is generated on-the-fly, so that just specific functionalities are active according to the current clinical scenario. Such a solution provides also a tight integration with the DICOM standard, which considers various aspects of the technology in medicine but does not address GUI specification issues. The proposed workflow is designed for diagnostic workstations with a local file system on an interchange media acting inside or outside the hospital ward. Accordingly, the DICOMDIR conceptual data model, defined by a hierarchical structure, is exploited and extended to include the GUI information thanks to a new Information Object Module (IOM), which reuses the DICOM information model. The proposed framework exploits the DICOM standard representing an enabling technology for an auto-consistent solution in medical diagnostic applications. In this paper we present a detailed description of the framework, its software design, and a proof-of-concept implementation as a suitable plug-in of the OsiriX imaging software., (Copyright © 2018 Elsevier Inc. All rights reserved.)

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician's requirements in a radiotherapy environment.

Colors play a fundamental role for children, both in the everyday life and in education. They recognize the surrounding world, and play games making a large use of colors. They learn letters and numbers by means of colors. As a consequence, early diagnosis of color blindness is an crucial to support an individual affected by this visual perception alteration at the initial phase of his/her life. The diagnosis of red-green color deficiencies (protanopia or deuteranopia) is commonly accomplished by means of the Ishihara test, which consists of plates showing dots with different sizes where some of them compose numbers within a gamut of colors while the ones composing the background have different colors. In this paper, a web application written in javascript is presented, that implements a digital Ishihara-like test for pre-school aged children. Instead numbers or letters, It can transform any binary image representing animal shapes, or any other child-friendly shape, into an Ishihara-like image. This digital plate is not static. The operator can increment the dot density to improve the quality of the shape contour and the entire plate can be redrawn with different dot sizes/colors chosen randomly according to the color pattern of the test. Separate controls for brightness and saturation are implemented to calibrate the chromatic aspect of the background and foreground dots.

In this paper we present a Teledentistry system aimed to the Second Opinion task. It make use of a particular camera called intra-oral camera, also called dental camera, in order to perform the photo shooting and real-time video of the inner part of the mouth. The pictures acquired by the Operator with such a device are sent to the Oral Medicine Expert (OME) by means of a current File Transfer Protocol (FTP) service and the real-time video is channeled into a video streaming thanks to the VideoLan client/server (VLC) application. It is composed by a HTML5 web-pages generated by PHP and allows to perform the Second Opinion both when Operator and OME are logged and when one of them is offline.

In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.

Bias artifact corrupts MRIs in such a way that the image is afflicted by illumination variations. Some of the authors proposed the exponential entropy-driven homomorphic unsharp masking ( E(2)D-HUM) algorithm that corrects this artifact without any a priori hypothesis about the tissues or the MRI modality. Moreover, E(2)D-HUM does not care about the body part under examination and does not require any particular training task. People who want to use this algorithm, which is Matlab-based, have to set their own computers in order to execute it. Furthermore, they have to be Matlab-skilled to exploit all the features of the algorithm. In this paper, we propose to make such algorithm available as a service on a grid infrastructure, so that people can use it almost from everywhere, in a pervasive fashion, by means of a suitable user interface running on smartphones. The proposed solution allows physicians to use the E(2)D-HUM algorithm (or any other kind of algorithm, given that it is available as a service on the grid), being it remotely executed somewhere in the grid, and the results are sent back to the user's device. This way, physicians do not need to be aware of how to use Matlab to process their images. The pervasive service provision for medical image enhancement is presented, along with some experimental results obtained using smartphones connected to an existing Globus-based grid infrastructure.

RF-inhomogeneity correction is a relevant research topic in the field of magnetic resonance imaging (MRI). A volume corrupted by this artifact exhibits nonuniform illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR volumes scanned from different body parts without any a priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.

Segmentation techniques based on active contours algorithm are widely used in medical imaging. Unfortunately, they require a lot of parameters to be used and this can represent an issue for those physicians with not much informatics skills. This paper proposes a software tool which allows to segment multiple slice can differ organ extraction setting a small number of parameters. Moreover, the tool offers the functionality to perform a multiple segmentation the same time, so that an entire volume composed by hundreds slices can be segmented.

In this paper we present a method for the automatic extraction of blood vessels from retinal images, while capturing points of intersection/overlap and endpoints of the vascular tree. The algorithm performance is evaluated through a comparison with handmade segmented images available on the STARE project database (STructured Analysis of the REtina). The algorithm is performed on the green channel of the RGB triad. The green channel can be used to represent the illumination component. The matched filter is used to enhance vessels w.r.t. the background. The separation between vessels and background is accomplished by a threshold operator based on gaussian probability density function. The length filtering removes pixels and isolated segments from the resulting image. Finally endpoints, intersections and overlapping vessels are extracted.

Magnetic Resonance images are often characterized by irregularly displaced luminance fluctuations that are called bias artifact. This disturb is due to a drop in signal intensity caused by the distance between imaged sample and receiver coil. An original approach to bias removal in Magnetic Resonance images is presented, which is based on the use of Gabor filter to extract the artifact. The proposed technique restores the image using a correction model, which is derived from the attenuation of signal diffusion across the tissues. No hypotheses are made about the structure of the tissues under investigation and the used MR spectrum. The approach is presented in detail, and extensive experimental results are reported along with a comparison with other popular techniques for bias removal.

Objective: An important artifact corrupting Magnetic Resonance Images is the rf inhomogeneity, also called bias artifact. This anomaly produces an abnormal illumination fluctuation on the image, due to variations of the device magnetic field. This artifact is particularly strong on images acquired with a device specialized on upper and lower limbs due to their coil configuration. A method based on homomorphic filtering aimed to suppress this artifact was proposed by Guillemaud. This filter has two faults: it doesn't provide an indication about the cutoff frequency (cf) and introduces another illumination artifact on the edges of the foreground. This work is an improvement to this method because it resolves both problems., Methods: The experimental setup has been performed on knee images obtained by 5 volunteers and acquired through an Artoscan device using the following parameters: Spin Echo sequence, Repetition time: 980 ms, Echo time: 26 ms, Slice thickness: 4 mm, Flip Angle: 90 degrees ., Results: Two specialists in orthoptics evaluated the results of the proposed approach by examining the restored images and validating the results produced by the filter. A quantitative evaluation has been performed on a manually segmented restored image using the coefficient of variation (cv) measure., Conclusions: Following the specialists qualitative evaluation, the illuminance of upper and lower peripheral zones results to be enhanced; a loose of contrast can be noted only in few cases. The Bias image exhibits an artifact focused usually on the central part of the foreground. The quantitative evaluation based on cv shows that this index is lowered for all the segmented regions with respect to the original value. The method is automatic and doesn't require any hypothesis on the tissues. A manual version of the algorithm can be also implemented allowing the physician to choose the preferred cf. In this case the value selected by the method can be considered as a default value.

This paper presents an improvement to the exponential entropy driven-homomorphic unsharp masking (E(2)D-HUM) algorithm devoted to illumination artifact suppression on magnetic resonance images. E(2)D-HUM requires a segmentation step to remove dark regions in the foreground whose intensity is comparable with background, because strong edges produce streak artifacts on the tissues. This new version of the algorithm keeps the same good properties of E(2)D-HUM without a segmentation phase, whose parameters should be chosen in relation to the image.

Purpose: To illustrate how a thorough neurophysiological evaluation allows early diagnosis and prognosis of traumatic brachial plexus injuries., Methods: As examples, we report on the case of two patients with acute traumatic brachial plexus injuries for whom a comprehensive neurophysiological evaluation allowed early diagnosis and prognosis., Results: Neurophysiological findings were consistent with complete proximal (root) lesion in one patient and a severe neuroapraxic block between Erb point and axilla in the other one., Conclusions: The two reported acute cases of traumatic brachial plexus injuries demonstrated the high diagnostic and prognostic value of the neurophysiological tests when fully utilized.

A very important artifact corrupting Magnetic Resonance Images is the RF inhomogeneity. This kind of artifact generates variations of illumination which trouble both direct examination by the doctor and segmentation algorithms. Even if homomorphic filtering approaches have been presented in literature, none of them has developed a measure to determine the cut-off frequency. In this work we present a measure based on information theory with a large experimental setup aimed to demonstrate the validity of our approach.

In this work we studied the inguinal-abdominal region and the inguinal canal using three-dimensional geometrical models. We built the models through computer aided geometric modeling techniques on the basis of observations during real dissections, operations and diagnostic medical imaging. The obtained models show in a complete modular synthesis and with a schematic iconology the structural organization of the anatomical districts in a logic sequence of layers and topographic and spatial relationships among its components. The models represent an amazing support to anatomy and clinical anatomy for teaching and research purposes on organogenesis, surgery and diagnosis.

The aim of this work was to develop and employ software for the study of the common carotid artery and its branches in the neck. We first constructed geometric models of anatomic components, for the building of knowledge bases to be used in automatic feature extraction techniques from images. Then, we developed a software system characterised by three-dimensional visualisation, image processing functions, knowledge base and a programming language to describe classification processing by means of fuzzy logic. The software system was used to perform automatic reconstruction of three-dimensional models of carotid arteries from contiguous CT scans of the neck. These findings suggest that such software systems represent a useful help in the study of image based diagnosis of carotid arteries.

A case of intrasacral meningocele associated to L4-L5 discal rupture is described. The possibility for this kind of malformation to be asymptomatic is emphasized.

Aneurysms of the petrous portion of the internal carotid artery are quite rare lesions. The case of a 60-year-old man who showed recurrent spontaneous otorrhagia caused by a petrous carotid aneurysm of considerable size is reported here. Mycotic origin of the lesion is presumed. Common carotid ligation was effective in resolving bleeding and did not cause any permanent neurological deficit. Other surgical procedures are acceptable.

Authors refer about the pathogenesis, the clinical and therapeutical problems, concerning two patients affected by mycotic intracranial aneurysms. They describe the anamnestical data of M.I.A., the clinic, the diagnostical and therapeutical measures in use.